Machine Learning To Help Rural Households Access Electricity

ObjectivesUse historical customer payment behavior data to predict which new applicants are likely to be a good fit for their pay-as-you-go solar power systems program Findings Customers that make three or fewer payments in its first 180 days have a 60% chance of having their system be repossessed. The team built a model that could predict whether a potential customer is likely or unlikely to keep up with payments. By using this methodology to approve customers, Simpa could have the potential to reduce delayed payments by almost one third (18% to 12.5%) while still accepting around 70% of the total customer pool. Question “With extra light, an additional 3 hours business fetches me Rs150-200 daily.”~Wasim Ahmed, a Simpa Networks customer Worldwide, approximately 1.6 billion people don’t have access to electricity…


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